考虑植被端元变异性的光谱解混方法

韦钦桦; 罗文斐; 唐凯丰

doi:10.11834/jrs.20210464

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考虑植被端元变异性的光谱解混方法
Spectral unmixing method considering endmember variability of vegetation
2023年27卷第2期页码：456-470
DOI： 10.11834/jrs.20210464

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韦钦桦, 罗文斐, 唐凯丰. 考虑植被端元变异性的光谱解混方法[J]. 遥感学报, 2023,27(2):456-470.

WEI Qinhua, LUO Wenfei, TANG Kaifeng. Spectral unmixing method considering endmember variability of vegetation[J]. National Remote Sensing Bulletin, 2023,27(2):456-470.
韦钦桦, 罗文斐, 唐凯丰. 考虑植被端元变异性的光谱解混方法[J]. 遥感学报, 2023,27(2):456-470. DOI： 10.11834/jrs.20210464.

WEI Qinhua, LUO Wenfei, TANG Kaifeng. Spectral unmixing method considering endmember variability of vegetation[J]. National Remote Sensing Bulletin, 2023,27(2):456-470. DOI： 10.11834/jrs.20210464.

摘要

植被光谱变异性广泛存在于遥感图像当中，本文尝试通过PROSAIL辐射传输模型来描述植被端元变异性，并提出一种光谱解混方法，实现逐像元地估计植被变异性端元。具体地，面向植被—土壤背景两端元的场景，在非负矩阵分解框架下，利用PROSAIL辐射传输模型从机理上描述植被端元的变异性，并通过两组神经网络来分别实现辐射传输模型的反演与正算，从而更高效地拟合植被端元，最终得到一种能逐像元求解变异性植被端元的光谱解混算法。由于该方法求解了植被端元的空间变异光谱，因此，能够对植被参数遥感反演的尺度效应进行纠正。为此，本文进一步以LAI尺度效应为例，通过无人机图像实验来验证该方法的有效性。实验结果得出，经过光谱解混方法处理后，该方法能较准确地估计植被端元，并能使LAI尺度效应均方根误差RMSE能够从0.2151降低到0.0896，有望提升遥感植被信息提取的精度。

Abstract

As ground features are affected by various factors, the problem of endmember variability will occur. Endmember variability greatly affects the accuracy of spectral unmixing results. This study is based on the vegetation-soil binary scene, and spectral unmixing is performed under the framework of NMF (Nonnegative Matrix Factorization). The PROSAIL model is used to describe the variability of vegetation endmembers from the mechanism so that the results of spectral unmixing have clear physical meaning. To improve efficiency, we set up two neural networks for model calculation and model inversion. In this way, the spectral unmixing algorithm can obtain the endmembers of the vegetation pixel by pixel, which can more accurately describe the variability of the vegetation endmember.,In addition, there is diversity in the spatial resolution of remote sensing data products. The problem of the scale effect is widespread and is a key issue in the field of remote sensing. Analysis of the reason is largely due to the mixed pixels that universally exist. The method studied in this paper describes the variability in the vegetation endmember. A spectral unmixing algorithm that can describe the variability of vegetation endmembers pixel by pixel is obtained. This result can be used to invert vegetation parameters. Therefore, this study attempts to correct the scale effect of remote sensing products by considering the spectral unmixing method of vegetation endmember variability.,This paper takes the LAI scale effect as an example. The effectiveness of the method is verified by an Unmanned Aerial Vehicle (UAV) image experiment. Three subimages were selected, and then they were resampled to two different levels of spatial resolution for experimentation. Among them, exponential function fitting is performed through the simulated spectrum of the PROSAIL model, and the relationship model is constructed to invert the LAI. The experimental results show that (1) the spectral unmixing method that uses the PROSAIL model to describe the variability of vegetation endmembers can obtain higher unmixing accuracy; (2) after using this spectral unmixing method, the Root Mean Square Error (RMSE) of the LAI scale effect is significantly reduced, and it has a certain effect on the correction of the LAI scale effect. This can improve the remote sensing scale effect problem to a certain extent.,In summary, the spectral unmixing method has a certain effect on the correction of LAI scale differences and can improve the problem of the remote sensing scale effect to a certain extent. However, the research has the following issues that need further consideration: (1) This article only considers the vegetation-soil binary scene, but this method has not verified the multiple endmember scene. (2) The variability of soil and other background endmembers can be further considered. (3) The model can be further optimized to reduce the difference between the PROSAIL model spectrum and the real image spectrum, thereby improving the accuracy of unmixing. (4) In this paper, the evaluation is carried out by the method of upscaling. In the future, more complicated practical factors can be considered for evaluation. Simultaneously, real images collected at different flight altitudes can also be used for evaluation.

关键词

遥感光谱解混端元变异性PROSAIL模型叶面积指数神经网络尺度效应

Keywords

remote sensingspectral unmixingendmember variabilityPROSAIL modelLeaf Area Indexneural networkscale effect

references

Asner G P, Bustamante M M C and Townsend A R. 2003. Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon. Remote Sensing of Environment, 87(4): 507-520 [DOI: 10.1016/j.rse.2003.03.001http://dx.doi.org/10.1016/j.rse.2003.03.001]

Bateson C A, Asner G P and Wessman C A. 2000. Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 1083-1094 [DOI: 10.1109/36.841987http://dx.doi.org/10.1109/36.841987]

Berry M W, Browne M, Langville A N, Pauca V P and Plemmons R J. 2007. Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics and Data Analysis, 52(1): 155-173 [DOI: 10.1016/j.csda.2006.11.006http://dx.doi.org/10.1016/j.csda.2006.11.006]

Chen Y H, Zhang W C and Yong B. 2007. Retrieving leaf area index using a neural network based on classification knowledge. Acta Ecologica Sinica, 27(7): 2785-2793

陈艳华, 张万昌, 雍斌. 2007. 基于分类知识利用神经网络反演叶面积指数. 生态学报, 27(7): 2785-2793 [DOI: 10.3321/j.issn:1000-0933.2007.07.015http://dx.doi.org/10.3321/j.issn:1000-0933.2007.07.015]

Drumetz L, Chanussot J and Jutten C. 2016a. Variability of the endmembers in spectral unmixing: recent advances//8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Los Angeles: IEEE: 1-5 [DOI: 10.1109/WHISPERS.2016.8071675http://dx.doi.org/10.1109/WHISPERS.2016.8071675]

Drumetz L, Veganzones M A, Henrot S, Phlypo R, Chanussot J and Jutten C. 2016b. Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability. IEEE Transactions on Image Processing, 25(8): 3890-3905 [DOI: 10.1109/TIP.2016.2579259http://dx.doi.org/10.1109/TIP.2016.2579259]

Du X X, Zare A, Gader P and Dranishnikov D. 2014. Spatial and spectral unmixing using the beta compositional model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 1994-2003 [DOI: 10.1109/JSTARS.2014.2330347http://dx.doi.org/10.1109/JSTARS.2014.2330347]

Eches O, Dobigeon N, Mailhes C and Tourneret J Y. 2010. Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery. IEEE Transactions on Image Processing, 19(6): 1403-1413 [DOI: 10.1109/TIP.2010.2042993http://dx.doi.org/10.1109/TIP.2010.2042993]

Eismann M T and Hardie R C. 2004. Stochastic spectral unmixing with enhanced endmember class separation. Applied Optics, 43(36): 6596-6608 [DOI: 10.1364/AO.43.006596http://dx.doi.org/10.1364/AO.43.006596]

Feret J B, François C, Asner G P, Gitelson A A, Martin R E, Bidel L P R, Ustin S L, Le Maire G and Jacquemoud S. 2008. PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6): 3030-3043 [DOI: 10.1016/j.rse.2008.02.012http://dx.doi.org/10.1016/j.rse.2008.02.012]

Gillis N and Glineur F. 2010. Using underapproximations for sparse nonnegative matrix factorization. Pattern Recognition, 43(4): 1676-1687 [DOI: 10.1016/j.patcog.2009.11.013http://dx.doi.org/10.1016/j.patcog.2009.11.013]

Guo Y K, Liu Y L, Zhang X J and Xu M. 2020. LAI inversion using radiation transfer model and random forest regression. Engineering of Surveying and Mapping, 29(3): 33-38

郭云开, 刘雨玲, 张晓炯, 许敏. 2020. 利用辐射传输模型和随机森林回归反演LAI. 测绘工程, 29(3): 33-38 [DOI: 10.19349/j.cnki.issn1006-7949.2020.03.007http://dx.doi.org/10.19349/j.cnki.issn1006-7949.2020.03.007]

Hasan U, Sawut M, Chen S S and Li D. 2020. Inversion of leaf area index of winter wheat based on GF-1/2 image. Acta Agronomica Sinica, 46(5): 787-797

吾木提·艾山江, 买买提·沙吾提, 陈水森, 李丹. 2020. 基于GF-1/2卫星数据的冬小麦叶面积指数反演. 作物学报, 46(5): 787-797 [DOI: 10.3724/SP.J.1006.2020.91049http://dx.doi.org/10.3724/SP.J.1006.2020.91049]

Heinz D, Chang C I and Althouse M L G. 1999. Fully constrained least-squares based linear unmixing [hyperspectral image classification]//IEEE 1999 International Geoscience and Remote Sensing Symposium. Hamburg: IEEE: 1401-1403 [DOI: 10.1109/IGARSS.1999.774644http://dx.doi.org/10.1109/IGARSS.1999.774644]

Hong D F, Yokoya N, Chanussot J and Zhu X X. 2019. An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Transactions on Image Processing, 28(4): 1923-1938 [DOI: 10.1109/TIP.2018.2878958http://dx.doi.org/10.1109/TIP.2018.2878958]

Jacquemoud S and Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91 [DOI: 10.1016/0034-4257(90)90100-Zhttp://dx.doi.org/10.1016/0034-4257(90)90100-Z]

Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, François C and Ustin S L. 2009. PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113 Suppl 1: S56-S66 [DOI: 10.1016/j.rse.2008.01.026http://dx.doi.org/10.1016/j.rse.2008.01.026]

Jiang J L, Ji X S, Yao X, Tian Y C, Zhu Y, Cao W X and Cheng T. 2018. Evaluation of three techniques for correcting the spatial scaling bias of leaf area index. Remote Sensing, 10(2): 221 [DOI: 10.3390/rs10020221http://dx.doi.org/10.3390/rs10020221]

Lee D D and Seung H S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788-791 [DOI: 10.1038/44565http://dx.doi.org/10.1038/44565]

Li J J, Zhu X H, Ma L L, Zhao Y G, Qian Y G and Tang L L. 2017. Leaf area index retrieval and scale effect analysis of multiple crops from UAV-based hyperspectral data. Remote Sensing Technology and Application, 32(3): 427-434

李剑剑, 朱小华, 马灵玲, 赵永光, 钱永刚, 唐伶俐. 2017. 基于无人机高光谱数据的多类型混合作物LAI反演及尺度效应分析. 遥感技术与应用, 32(3): 427-434 [DOI: 10.11873/j.issn.1004-0323.2017.3.0427http://dx.doi.org/10.11873/j.issn.1004-0323.2017.3.0427]

Li X C, Xu X G, Bao Y S, Huang W J, Luo J H, Dong Y Y, Song X Y and Wang J H. 2012. Retrieving LAI of winter wheat based on sensitive vegetation index by the segmentation method. Scientia Agricultura Sinica, 45(17): 3486-3496

李鑫川, 徐新刚, 鲍艳松, 黄文江, 罗菊花, 董莹莹, 宋晓宇, 王纪华. 2012. 基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演. 中国农业科学, 45(17): 3486-3496 [DOI: 10.3864/j.issn.0578-1752.2012.17.004http://dx.doi.org/10.3864/j.issn.0578-1752.2012.17.004]

Li X W. 2005. Retrospect, prospect and innovation in quantitative remote sensing. Journal of Henan University (Natural Science), 35(4): 49-56

李小文. 2005. 定量遥感的发展与创新. 河南大学学报(自然科学版), 35(4): 49-56 [DOI: 10.3969/j.issn.1003-4978.2005.04.012http://dx.doi.org/10.3969/j.issn.1003-4978.2005.04.012]

Li X W. 2006. Review of the project of quantitative remote sensing of major factors for spatial-temporal heterogeneity on the land surface. Advances in Earth Science, 21(8): 771-780

李小文. 2006. 地球表面时空多变要素的定量遥感项目综述. 地球科学进展, 21(8): 771-780 [DOI: 10.3321/j.issn:1001-8166.2006.08.001http://dx.doi.org/10.3321/j.issn:1001-8166.2006.08.001]

Li X W, Wang J D and Strahler A H. 1999. Scale effect of Planck’s law over nonisothermal blackbody surface. Science in China Series E: Technological Sciences, 42(6): 652-656

李小文, 王锦地, Strahler A H. 1999. 非同温黑体表面上Planck定律的尺度效应. 中国科学(E辑), 29(5): 422-426 [DOI: 10.1007/BF02917003http://dx.doi.org/10.1007/BF02917003]

Li X W, Wang J D and Strahler A H. 2000. Scale effects and scaling-up by geometric-optical model. Science in China Series E: Technological Sciences, 43(1): 17-22

李小文, 王锦地, Strahler A H. 2000. 尺度效应及几何光学模型用于尺度纠正. 中国科学(E辑), 30(S1): 12-17 [DOI: 10.3321/j.issn:1006-9275.2000.z1.002http://dx.doi.org/10.3321/j.issn:1006-9275.2000.z1.002]

Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169

李小文, 王祎婷. 2013. 定量遥感尺度效应刍议. 地理学报, 68(9): 1163-1169 [DOI: 10.11821/dlxb201309001http://dx.doi.org/10.11821/dlxb201309001]

Li X W, Zhao H R, Zhang H and Wang J D. 2002. Global change study and quantitative remote sensing for land surface parameters. Earth Science Frontiers, 9(2): 365-370

李小文, 赵红蕊, 张颢, 王锦地. 2002. 全球变化与地表参数的定量遥感. 地学前缘, 9(2): 365-370 [DOI: 10.3321/j.issn:1005-2321.2002.02.015http://dx.doi.org/10.3321/j.issn:1005-2321.2002.02.015]

Li Y N, Lu L and Liu Y. 2017. Tasseled cap triangle (TCT)-leaf area index (LAI) model of rice fields based on PROSAIL model and its application. Chinese Journal of Applied Ecology, 28(12): 3976-3984

李亚妮, 鲁蕾, 刘勇. 2017. 基于PROSAIL模型的水稻田缨帽三角—叶面积指数模型及其应用. 应用生态学报, 28(12): 3976-3984 [DOI: 10.13287/j.1001-9332.201712.016http://dx.doi.org/10.13287/j.1001-9332.201712.016]

Liang L, Di L P, Zhang L P, Deng M X, Qin Z H, Zhao S H and Lin H. 2015. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 165: 123-134 [DOI: 10.1016/j.rse.2015.04.032http://dx.doi.org/10.1016/j.rse.2015.04.032]

Lin H, Liang L, Zhang L P and Du P J. 2013. Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm. Transactions of the Chinese Society of Agricultural Engineering, 29(11): 139-146

林卉, 梁亮, 张连蓬, 杜培军. 2013. 基于支持向量机回归算法的小麦叶面积指数高光谱遥感反演. 农业工程学报, 29(11): 139-146 [DOI: 10.3969/j.issn.1002-6819.2013.11.018http://dx.doi.org/10.3969/j.issn.1002-6819.2013.11.018]

Liu L Y. 2014a. Vegetation Quantitative Remote Sensing Principles and Applications. Beijing: Science Press

刘良云. 2014a. 植被定量遥感原理与应用. 北京: 科学出版社

Liu L Y. 2014b. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158-1168

刘良云. 2014b. 叶面积指数遥感尺度效应与尺度纠正. 遥感学报, 18(6): 1158-1168 [DOI: 10.11834/jrs.20144103http://dx.doi.org/10.11834/jrs.20144103]

Ma J J and Yuan J G. 2014. Relationship between NDVI, view zenith angle and LAI based on model simulation. Remote Sensing Technology and Application, 29(4): 539-546

马晶晶, 袁金国. 2014. 基于模型模拟的植被NDVI与观测天顶角和LAI的关系. 遥感技术与应用, 29(4): 539-546 [DOI: 10.11873/j.issn.1004-0323.2014.4.0539http://dx.doi.org/10.11873/j.issn.1004-0323.2014.4.0539]

Ma Y J. 2019. Calibration of Regional MODIS LAI Products Considering the Difference of Model, Scale and Image Parameter. Beijing: China University of Geosciences (Beijing): 50-56

马雨佳. 2019. 顾及数据源、反演模型与尺度差异的区域MODIS LAI产品校正方法. 北京: 中国地质大学(北京): 50-56 [DOI: 10.27493/d.cnki.gzdzy.2019.001131http://dx.doi.org/10.27493/d.cnki.gzdzy.2019.001131]

Pan H Z and Chen Z X. 2018. Application of UVA hyperspectral remote sensing in winter wheat leaf area index inversion. Chinese Journal of Agricultural Resources and Regional Planning, 39(3): 32-37

潘海珠, 陈仲新. 2018. 无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用. 中国农业资源与区划, 39(3): 32-37 [DOI: 10.7621/cjarrp.1005-9121.20180305http://dx.doi.org/10.7621/cjarrp.1005-9121.20180305]

Pinty B, Lavergne T, Widlowski J L, Gobron N and Verstraete M M. 2009. On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption. Remote Sensing of Environment, 113(1): 10-23 [DOI: 10.1016/j.rse.2008.08.017http://dx.doi.org/10.1016/j.rse.2008.08.017]

Ren H Y, Zhao Y L, Li X B and Ge Y J. 2020. Cultivated land fragmentation in mountainous areas based on different resolution images and its scale effects. Geographical Research, 39(6): 1283-1294

任红玉, 赵宇鸾, 李秀彬, 葛玉娟. 2020. 基于不同分辨率影像的山区耕地细碎化评价及其尺度效应. 地理研究, 39(6): 1283-1294 [DOI: 10.11821/dlyj020190863http://dx.doi.org/10.11821/dlyj020190863]

Roberts D A, Gardner M, Church R, Ustin S, Scheer G and Green R O. 1998. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3): 267-279 [DOI: 10.1016/S0034-4257(98)00037-6http://dx.doi.org/10.1016/S0034-4257(98)00037-6]

Schmidt F, Schmidt A, Treguier E, Guiheneuf M, Moussaoui S and Dobigeon N. 2010. Implementation strategies for hyperspectral unmixing using bayesian source separation. IEEE Transactions on Geoscience and Remote Sensing, 48(11): 4003-4013 [DOI: 10.1109/TGRS.2010.2062190http://dx.doi.org/10.1109/TGRS.2010.2062190]

Somers B, Asner G P, Tits L and Coppin P. 2011. Endmember variability in spectral mixture analysis: a review. Remote Sensing of Environment, 115(7): 1603-1616 [DOI: 10.1016/j.rse.2011.03.003http://dx.doi.org/10.1016/j.rse.2011.03.003]

Tao H L, Feng H K, Yang G J, Yang X D, Liu M X and Liu S B. 2020. Leaf area index estimation of winter wheat based on UAV imaging hyperspectral imagery. Transactions of the Chinese Society for Agricultural Machinery, 51(1): 176-187

陶惠林, 冯海宽, 杨贵军, 杨小冬, 刘明星, 刘帅兵. 2020. 基于无人机成像高光谱影像的冬小麦LAI估测. 农业机械学报, 51(1): 176-187 [DOI: 10.6041/j.issn.1000-1298.2020.01.019http://dx.doi.org/10.6041/j.issn.1000-1298.2020.01.019]

Thouvenin P A, Dobigeon N and Tourneret J Y. 2016. Hyperspectral unmixing with spectral variability using a perturbed linear mixing model. IEEE Transactions on Signal Processing, 64(2): 525-538 [DOI: 10.1109/TSP.2015.2486746http://dx.doi.org/10.1109/TSP.2015.2486746]

Tian Y H, Woodcock C E, Wang Y J, Privette J L, Shabanov N V, Zhou L M, Zhang Y, Buermann W, Dong J R, Veikkanen B, Häme T, Andersson K, Ozdogan M, Knyazikhin Y and Myneni R B. 2002. Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sensing of Environment, 83(3): 414-430 [DOI: 10.1016/S0034-4257(02)00047-0http://dx.doi.org/10.1016/S0034-4257(02)00047-0]

Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16(2): 125-141 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]

Verhoef W, Jia L, Xiao Q and Su Z. 2007. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1808-1822 [DOI: 10.1109/TGRS.2007.895844http://dx.doi.org/10.1109/TGRS.2007.895844]

Wang L J and Niu Z. 2014. Sensitivity analysis of vegetation parameters based on PROSAIL model. Remote Sensing Technology and Application, 29(2): 219-223

王李娟, 牛铮. 2014. PROSAIL模型的参数敏感性研究. 遥感技术与应用, 29(2): 219-223 [DOI: 10.11873j.issn.1004-0323.2014.2.0219http://dx.doi.org/10.11873j.issn.1004-0323.2014.2.0219]

Wu H and Li Z L. 2009. Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors, 9(3): 1768-1793 [DOI: 10.3390/s90301768http://dx.doi.org/10.3390/s90301768]

Xiao Y F, Zhou D M, Gong H L and Zhao W J. 2015. Sensitivity of canopy reflectance to biochemical and biophysical variables. Journal of Remote Sensing, 19(3): 368-374

肖艳芳, 周德民, 宫辉力, 赵文吉. 2015. 冠层反射光谱对植被理化参数的全局敏感性分析. 遥感学报, 19(3): 368-374 [DOI: 10.11834/jrs.20153330http://dx.doi.org/10.11834/jrs.20153330]

Yang B,Luo W F.2015.Constrained NMF-based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from a hyper spectral remote sensing image.Journal of Remote Sensing, 19(2):240-253.

杨斌,罗文斐.约束非负矩阵分解框架下高维自适应粒子群端元提取.遥感学报,19(02):240-253 [DOI: 10.11834/jrs.20154096http://dx.doi.org/10.11834/jrs.20154096]

Yang Y W. 2018. LAI Scale Effect study in Heihe Oasis Based on CASI Data. Chengdu: Chengdu University of Technology: 5-54

杨雨薇. 2018. 基于CASI数据的黑河绿洲区LAI尺度效应研究. 成都: 成都理工大学: 5-54

Yu Q H, Yang G J and Wang C C. 2019. Chlorophyll inversion of winter wheat based on ground hyperspectral data and PROSAIL model. Science of Surveying and Mapping, 44(11): 96-102, 136

于汧卉, 杨贵军, 王崇倡. 2019. 地面高光谱和PROSAIL模型的冬小麦叶绿素反演. 测绘科学, 44(11): 96-102, 136 [DOI: 10.16251/j.cnki.1009-2307.2019.11.014http://dx.doi.org/10.16251/j.cnki.1009-2307.2019.11.014]

Zare A and Ho K C. 2014. Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing. IEEE Signal Processing Magazine, 31(1): 95-104 [DOI: 10.1109/MSP.2013.2279177http://dx.doi.org/10.1109/MSP.2013.2279177]

Zhan H M and Xu S Z. 2011. Generalized linear mixed model for segregation distortion analysis. BMC Genetics, 12: 97 [DOI: 10.1186/1471-2156-12-97http://dx.doi.org/10.1186/1471-2156-12-97]

Zhang R H, Sun X M, Su H B, Tang X Z and Zhu Z L. 1999. Remote sensing and scale transfering of levity parameters on earth surface. Remote Sensing for Land and Resources, 41(3): 51-58

张仁华, 孙晓敏, 苏红波, 唐新斋, 朱治林. 1999. 遥感及其地球表面时空多变要素的区域尺度转换. 国土资源遥感, 41(3): 51-58 [DOI: 10.3969/j.issn.1001-070X.1999.03.011http://dx.doi.org/10.3969/j.issn.1001-070X.1999.03.011]

Zhao C H, Cui S L and Liu W. 2014. Multi-endmember hierarchical mixture analysis algorithm for spectra. Journal of Optoelectronics·Laser, 25(9): 1830-1836

赵春晖, 崔士玲, 刘务. 2014. 基于分层的多端元光谱解混算法. 光电子·激光, 25(9): 1830-1836 [DOI: 10.16136/j.joel.2014.09.033http://dx.doi.org/10.16136/j.joel.2014.09.033]

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